Automatic layout and cutting method for yoga suit production cloth

Abstract

This invention relates to an automated pattern making and cutting method for yoga wear production fabrics. The method utilizes fabric micro-strain sensors, high-frequency acoustic signature pickups, and infrared thermal imaging modules deployed at the sewing station to collect real-time sewing physical data. A time-series neural network model is then used to generate a set of process tags, enabling process status monitoring and parameter quantification. The system employs a dual-buffered queue to associate cut pieces with process tags. When process adaptability falls below a threshold, it automatically triggers local reordering, dynamically selecting and replacing cut pieces with high edge anchoring stability, and adjusting the pattern making sequence based on a topology optimization algorithm. By embedding reordering operation logs and process feedback information, the solution achieves real-time output of the final cutting instruction and result auditing, significantly improving sewing stability, material utilization, and the adaptability of intelligent pattern making.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing and industrial automation pattern making technology for clothing, and in particular to an automatic pattern making and cutting method for yoga clothing production fabric. Background Technology

[0002] Currently, intelligent manufacturing and industrial automation nesting technologies in the apparel industry are widely used, and research on improving fabric utilization and optimizing production processes continues to deepen. Most mainstream automatic nesting systems in the industry employ static geometric optimization, heuristic search, linear / nonlinear programming, or multi-objective joint modeling to achieve high-density and high-efficiency arrangement of cut pieces on the fabric surface. These solutions typically focus on maximizing geometric space utilization and minimizing raw material consumption, supporting the sorting of cut pieces according to static rules such as area, outer contour, and splicing constraints. They generate nesting sequences and diagrams through batch calculations, and then output cutting control instructions. In recent years, with the digital transformation and upgrading of factories, flexible nesting engines with capabilities such as original cut piece management, order BOM parsing, and automatic cutting data integration have gradually become the basic modules of intelligent manufacturing production lines. Mainstream technologies have achieved certain results in improving material utilization, reducing reliance on manual labor, and shortening production cycle time.

[0003] In the context of multi-process integration in garment production, existing pattern making systems generally use fabric geometry as the boundary, treating subsequent processes such as sewing, overlocking, and ironing only as parallel links in the production line. Some high-end automated production lines have begun to introduce methods such as digital twins, physical simulation, and process weight adjustment to achieve overall balance in cycle time and capacity across processes through joint scheduling at the process level. However, they generally lack decision-making adjustments driven by underlying physical feedback, especially in real-time perception and dynamic adaptation of pattern making logic under complex physical coupling conditions such as sewing involving heat, force, and sound. Existing publicly available solutions also lack pattern sequence rearrangement behavior with real-time feedback from sewing; their automatic learning and response to multi-process constraints remain in the theoretical framework or local pilot stages.

[0004] Existing representative technologies primarily focus on optimizing geometrically optimal piece arrangement, spatial splicing, and material cutting. Typical applications include automated cutting in apparel, bags, automotive interiors, and technical textiles. These solutions can generate statically optimized layout schemes after order BOM analysis, and connect to various automated cutting equipment via standard communication protocols to ensure efficient production line operations. However, these technologies are mainly suitable for scenarios where process parameters are known, material properties are uniform, each stage is discretely segmented, and process fluctuations are small or can be pre-modeled. For high-end flexible manufacturing scenarios involving elastic fabrics, irregularly shaped pieces, tightly coupled processes, and large process fluctuations, existing geometry-driven layout technologies struggle to achieve simultaneous optimization of optimal material utilization and process stability.

[0005] Given the current state of technology, the following prominent problems and technical deficiencies exist: I. Most existing pattern layout algorithms are "one-time optimal". Their optimization process is based on the geometric parameters of the fabric and basically does not consider the real-time status feedback of subsequent processes such as sewing and cutting. This leads to the pattern layout scheme being out of touch with the stability of the actual process. Although some layout methods have high geometric utilization, physical bottlenecks such as edge curling and stitch deformation in the sewing process cause rework, waste or even downtime.

[0006] Second, current process constraints are mostly based on human experience, pre-modeling, or static parameters, lacking the ability to collect and dynamically respond to microscopic physical states such as sewing thread tension, fabric elasticity recovery, and edge anchoring. This leads to delayed discovery of process risks and makes it impossible to avoid potential hazards in advance through dynamic adjustments to the layout.

[0007] Third, although some high-end flexible production lines have introduced process-level information feedback and digital twin simulation, the feedback granularity is relatively coarse, the algorithm itself has a high response threshold, a large update delay, and lacks the ability to perform atomic-level displacement and piece reordering, making it impossible to make micro-decision and timely adjustments for each process fluctuation.

[0008] From the perspective of current industry needs, in the production scenarios of customized garments with high elasticity and high technological risks, such as yoga wear, if the pattern-making algorithm can dynamically adapt to the real-time feedback of the sewing station, it will effectively improve material utilization, finished product qualification rate, and production continuity, significantly enhancing the level of automated flexible manufacturing. Current technology has not yet solved the core problem of "how to directly convert the physical response of the sewing process into real-time input for the pattern-making algorithm, dynamically rearrange the cut piece sequence as needed, and proactively avoid geometric layout schemes that have been verified by the process as unmanufacturable."

[0009] Therefore, innovative technical solutions are urgently needed to achieve the adaptive capability of the nesting process to real-time constraints of multiple processes. This requires breaking through the traditional "static geometry-driven" nesting concept and establishing a new nesting-cutting system that uses physical feedback from sewing conditions as input, supports local reordering and closed-loop optimization, and truly connects the entire collaborative chain of intelligent processes in flexible manufacturing. This will not only greatly reduce scrap rates and material waste, but also lay a data foundation and provide decision support for production quality traceability, heterogeneous equipment linkage, and future smart garment factory applications. Summary of the Invention

[0010] This application provides an automatic pattern making and cutting method for yoga clothing production fabric, which aims to solve one of the problems or problems of the prior art mentioned in the background art.

[0011] This application provides an automatic pattern making and cutting method for yoga clothing production fabric, specifically including: S1: Deploy a sensor array at the yoga garment sewing station, including a fabric micro-strain thin film sensor, a high-frequency acoustic pickup, and an infrared hot spot imaging module, to synchronously collect multi-dimensional physical signals during the sewing process to obtain sewing condition data streams.

[0012] S2: Input the sewing condition data stream into a pre-trained temporal neural network model for feature mapping processing to generate a set of process labels including stitch extension tolerance, edge anchoring stability, and stitch density compatibility.

[0013] S3: Construct a main queue based on the pre-sorted set of cut pieces in descending order of area, and establish an auxiliary queue associated with the set of process labels to form a dual-queue architecture for storing cut pieces to be arranged and real-time feedback labels.

[0014] S4: Monitor the set of process tags in the auxiliary queue and determine whether the latest received suture extension tolerance value is lower than a preset stable threshold to trigger local reordering.

[0015] S5: If a local reordering is triggered, multiple minimum area cut pieces that have not yet been arranged are extracted from the tail of the main queue to form a set of candidate cut pieces, and alternative cut piece sets are selected according to the current edge anchoring stability level.

[0016] S6: Search for gap clusters that can accommodate the alternative pattern pieces within the non-critical stress zone of the already arranged layout, swap the positions of the alternative pattern pieces with the original pattern pieces in the gap clusters, and adjust the geometric arrangement to generate a pattern sequence.

[0017] S7: Based on the replaced piece identifier, insertion position coordinates, and gap cluster envelope parameters of the layout sequence, construct a reordering operation log containing the label matching degree improvement value and embed it into the header of the final layout image file.

[0018] S8: The rearranged pattern sequence after partial reordering is output as the final cutting instruction to the automatic cutting equipment to complete the intelligent control closed loop from fabric input to finished product output.

[0019] The automatic pattern making and cutting method for yoga clothing production fabric provided in this application has the following beneficial effects: (1) By deploying a lightweight sensor array at a typical sewing station in yoga clothing, and integrating multi-dimensional physical signals such as fabric micro-strain, high-frequency acoustic signature, and infrared hot spot, this invention achieves real-time perception and structural deconstruction of material behavior during sewing for the first time, effectively overcoming the technical defects of traditional nesting systems that rely solely on static geometric parameters and ignore the dynamic response of downstream processes. This solution uses a pre-trained lightweight temporal neural network to perform online analysis of the original signal stream, generating a set of process labels containing three dimensions: "seam extension tolerance," "edge anchoring stability," and "stitch density compatibility." This transforms the originally invisible micro-sewing risks into calculable and decision-making structured input variables, significantly improving the adaptability of the nesting solution to the actual production environment. Compared with the offline experience modeling or multi-objective weighted optimization methods commonly used in existing technologies, this invention avoids recommendation bias caused by the solidification of process parameters or subjective weight settings, effectively enhancing the process credibility and execution robustness of the nesting results.

[0020] (2) A local reordering mechanism driven by a dual-buffered queue is introduced, which realizes dynamic adaptive adjustment of the layout process without changing the original order BOM structure. This breaks through the rigid process limitation that traditional layout algorithms cannot respond to on-site feedback once started. When the system detects that a newly accessed set of process tags indicates that the current piece combination has a low extensibility tolerance risk, it automatically triggers the extraction of the smallest area piece from the tail of the main queue and filters the replacement subset according to the edge anchoring stability matching rule. Combined with the fast position replacement algorithm under topological constraints, it accurately searches for gap clusters that can accommodate replacement pieces in the non-critical stress area of ​​the already laid-out area, completing the local optimization closed loop. This mechanism completes the entire process of perception-decision-reordering within hundreds of milliseconds, with the response delay controlled within 200 milliseconds. It not only ensures the continuity of layout but also realizes dynamic correction for real sewing performance, greatly reducing the rework rate and waste piece generation caused by material property fluctuations, and significantly improving fabric utilization and production consistency.

[0021] (3) The entire reordering process is autonomously scheduled by the embedded nesting controller. All operation logs are embedded as metadata in the header of the final nesting diagram file, forming a traceable and auditable process decision-making link. This not only provides high-value data support for subsequent quality backtracking and process optimization, but also builds a closed-loop adaptive system driven by feedback from the physical world to update digital decisions. This design does not require the introduction of manual intervention or external scheduling system coupling, avoiding the common problems of model synchronization lag and computing resource overload in digital twin architectures. It has good engineering implementation and system lightweight advantages. More importantly, this solution completely gets rid of the dependence on multi-objective joint modeling and complex weight parameter tuning, and instead takes the physical response in the actual sewing process as the direct driving force, promoting the nesting logic to realize the essential leap from "pursuing geometric optimality" to "ensuring process reliability", providing a new technical paradigm for intelligent production scheduling in the flexible fabric manufacturing scenario.

[0022] In summary, this solution constructs a highly responsive, closed-loop autonomous, and process-oriented intelligent nesting system through three core mechanisms: edge perception forwarding, process label mapping, and local dynamic rearrangement. This not only significantly improves the practical feasibility and material matching accuracy of the nesting solution, but also achieves information connectivity and collaborative optimization between the manufacturing front end and the sewing end without increasing hardware costs or labor burden. It has outstanding innovation, practicality, and promotional value. Attached Figure Description

[0023] Figure 1 This is the main flowchart of an automated pattern making and cutting method for yoga clothing production fabrics.

[0024] Figure 2 This is a sub-flowchart of an automated pattern making and cutting method for yoga clothing production fabrics.

[0025] Figure 3 This is another sub-flowchart of an automated pattern making and cutting method for yoga clothing production fabrics. Detailed Implementation

[0026] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0027] The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. Of course, these are merely examples and are not intended to limit the invention. Furthermore, reference numerals and / or letters may be repeated in different examples; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0028] like Figure 1 As shown, this application provides an automatic pattern making and cutting method for yoga clothing production fabric, specifically including: S1: Deploy a sensor array at the yoga garment sewing station, including a fabric micro-strain thin film sensor, a high-frequency acoustic pickup, and an infrared hot spot imaging module, to synchronously collect multi-dimensional physical signals during the sewing process to obtain sewing condition data streams.

[0029] S2: Input the sewing condition data stream into a pre-trained temporal neural network model for feature mapping processing to generate a set of process labels including stitch extension tolerance, edge anchoring stability, and stitch density compatibility.

[0030] S3: Construct a main queue based on the pre-sorted set of cut pieces in descending order of area, and establish an auxiliary queue associated with the set of process labels to form a dual-queue architecture for storing cut pieces to be arranged and real-time feedback labels.

[0031] S4: Monitor the set of process tags in the auxiliary queue and determine whether the latest received suture extension tolerance value is lower than a preset stable threshold to trigger local reordering.

[0032] S5: If a local reordering is triggered, multiple minimum area cut pieces that have not yet been arranged are extracted from the tail of the main queue to form a set of candidate cut pieces, and alternative cut piece sets are selected according to the current edge anchoring stability level.

[0033] S6: Search for gap clusters that can accommodate the alternative pattern pieces within the non-critical stress zone of the already arranged layout, swap the positions of the alternative pattern pieces with the original pattern pieces in the gap clusters, and adjust the geometric arrangement to generate a pattern sequence.

[0034] S7: Based on the replaced piece identifier, insertion position coordinates, and gap cluster envelope parameters of the layout sequence, construct a reordering operation log containing the label matching degree improvement value and embed it into the header of the final layout image file.

[0035] S8: The rearranged pattern sequence after partial reordering is output as the final cutting instruction to the automatic cutting equipment to complete the intelligent control closed loop from fabric input to finished product output.

[0036] Step S1: Deploy a sensor array at the yoga garment sewing station, comprising a fabric micro-strain thin-film sensor, a high-frequency acoustic signature pickup, and an infrared hotspot imaging module, to synchronously acquire multi-dimensional physical signals during the sewing process to obtain a sewing condition data stream. Specifically, this includes: S1.1: A fabric micro-strain thin-film sensor is attached and deployed above the feed dog in a typical sewing station of yoga clothing. Based on the principle of piezoresistive effect, the micro-deformation electrical signal of the fabric under sewing traction is acquired in real time to generate the original voltage sequence of fabric micro-strain that includes instantaneous elongation and rebound hysteresis characteristics.

[0037] A stable attachment position is selected above the feed dog at a typical yoga garment sewing station, ensuring the fabric feed path perfectly aligns with the sensor's sensitive area. Surface pretreatment is performed at this position, using plasma cleaning to remove dust and oil, improving the adhesion reliability and signal stability of the thin-film sensor. The fabric micro-strain thin-film sensor is attached according to a preset geometric orientation, aligning its measurement axis with the fabric fiber direction to maximize the piezoresistive effect's response to fiber stretching. During attachment, a constant pressure rolling process eliminates air gaps between the sensor and the fabric, forming a uniform mechanical coupling interface. The sensor terminals are connected to the high-impedance amplification input of the edge acquisition module via shielded wires to prevent environmental electromagnetic interference from degrading the piezoresistive signal. A stable bias voltage is continuously applied inside the acquisition module, converting the microscopic deformation of the fabric under sewing traction into resistance changes through the piezoresistive effect, and outputting the corresponding instantaneous voltage value in real time. A high-speed analog-to-digital acquisition chip captures continuous voltage data at millisecond-level sampling periods, forming a raw voltage sequence encompassing instantaneous stretch rate and rebound hysteresis characteristics. The formula for calculating the elongation ratio R is used as a reference, where the elongation ratio can be obtained by the following formula: In the formula, This is the current fabric length. For the initial length, The instantaneous elongation rate is used; then the rebound hysteresis characteristics are evaluated by the area of ​​the hysteresis curve of the continuous sampling values, so as to quantify the dynamic mechanical behavior of the fabric during the traction and relaxation process.

[0038] Through the above chain processing method, the piezoresistive response obtained by the sensor is converted into the original voltage sequence of fabric micro-strain with physical dimensions, so as to realize the accurate real-time capture of the micro-deformation of the fabric under sewing traction.

[0039] For example, a fabric micro-strain thin-film sensor, 30mm long, 5mm wide, and 0.1mm thick, is attached above a 40mm wide feed dog. The sensor's measuring axis is perfectly aligned with the warp direction of the fabric. Surface pretreatment involves 50W plasma cleaning for 20 seconds. After attachment, a 2N rolling pressure is applied to ensure uniform adhesion. The acquisition module's bias voltage is set to 3.3V, and the sampling period is 1ms. During sewing, the initial fabric length L0 is 120.00mm, and the instantaneous length L changes to 121.20mm at the stitch length. Substituting these values ​​into the formula, the instantaneous elongation rate R is calculated to be 0.010, or 1.0%. In the rebound stage, analysis of the hysteresis curve area of ​​the voltage sequence reveals that the rebound hysteresis quantification value is significantly higher than the baseline value for the flat stitch process, demonstrating that the fabric at this location possesses a high fiber elastic deformation recovery capability. The output original voltage sequence maintains millisecond-level continuity during subsequent analog-to-digital conversion, significantly improving the accuracy and stability of process tag set generation.

[0040] S1.2: The original voltage sequence of micro-strain on the fabric surface is subjected to analog-to-digital conversion and timestamp alignment. The mechanical vibration sound wave signal at the moment the needle bar pierces the fabric is synchronously acquired using a high-frequency acoustic fingerprint pickup to generate a multi-channel acoustic fingerprint amplitude waveform data stream with a unified time base.

[0041] An analog-to-digital conversion operation is performed on the original voltage sequence of micro-strain on the fabric surface. A high-precision Σ-Δ analog-to-digital converter is used to map the continuous voltage signal generated by the piezoresistive effect into a discrete digital quantity. The sampling frequency is set to be consistent with that of the preceding sensor to ensure time-domain consistency.

[0042] The digital sequence after analog-to-digital conversion is timestamped with the internal clock signal of the sensor. A high-stability crystal oscillator reference source is used to correct the sampling time base drift. A frame alignment algorithm is used to ensure the unified timing of subsequent multi-source signals.

[0043] The piezoelectric transducer of the high-frequency acoustic pickup is activated to generate a mechanical vibration acoustic wave signal at the moment the needle bar penetrates the fabric. The sampling frequency range is set according to the sensor specifications to cover the main harmonic frequency bands of the needle bar movement.

[0044] The analog acoustic wave signal collected by the acoustic fingerprint pickup is converted into a multi-channel digital amplitude data stream by an analog-to-digital converter. The unified timestamp is bound by the inter-channel synchronous triggering mechanism, forming a registerable dual signal source input with the fabric micro-strain data under the same time base.

[0045] The multi-channel data synthesis architecture is invoked to combine the amplitude sequences of each voiceprint acquisition channel into a unified format multi-channel voiceprint amplitude waveform data stream in chronological order, laying the foundation for subsequent frequency domain filtering and multimodal feature fusion.

[0046] By using analog-to-digital conversion and timestamp alignment, the original voltage sequence of fabric micro-strain from the previous step is converted into a precise digital signal and matched with the acoustic signature signal at the moment of needle puncture under the same time base, generating a multi-channel acoustic signature amplitude waveform data stream that can be directly used for multi-source fusion analysis, thus achieving the spatiotemporal consistency required for subsequent fusion of infrared hot spots and heterogeneous signals.

[0047] For example, on a yoga garment sewing production line, the voltage sequence output from the fabric micro-strain thin-film sensor is sampled at a frequency of 4800Hz, with a Σ-Δ analog-to-digital converter (ADC) resolution set to 24 bits, and a crystal oscillator frequency of 10MHz used for timestamp binding. A high-frequency acoustic signature pickup is used for the needle bar puncture action, with a sampling frequency set to 44.1kHz, covering the main high-frequency harmonics of the needle bar vibration. The acoustic signature signal is simultaneously acquired from different locations via an 8-channel acquisition module and bound to a unified timestamp. After analog-to-digital conversion, the two types of data undergo frame registration in a time-base correction module, with the error controlled within 0.1ms. The final multi-channel acoustic signature amplitude waveform data stream, after normalization encoding, has a length of 1024 samples per channel, and the amplitude range is uniformly normalized, forming formatted input data suitable for multimodal signal fusion. In this scenario, the spatiotemporal synchronization error is significantly reduced during subsequent frequency domain filtering and infrared hotspot fusion, ensuring the accuracy of the generated process label set.

[0048] S1.3: The multi-channel acoustic waveform amplitude data stream is subjected to frequency domain filtering and noise reduction processing. At the same time, the infrared hot spot imaging module is activated to monitor the instantaneous temperature rise distribution in the suture area. The pixel-level temperature gradient matrix is ​​calculated based on the thermal radiation law to generate an infrared hot spot temperature field image frame sequence that characterizes the frictional heat effect of the suture.

[0049] Frequency domain filtering and denoising were performed on the multi-channel acoustic waveform data stream. Fast Fourier Transform (FFT) was used to decompose the spectrum of each acoustic channel data and construct an amplitude spectrum matrix. A bandpass filter window function was applied to areas of concentrated spectral energy to remove high-frequency and low-frequency noise components exceeding the characteristic frequency band of the sewing machine's mechanical vibration. An inverse Fourier transform was performed based on the filtered amplitude spectrum matrix to recover the time-domain signal. The recovered acoustic waveform data was then resampled using a unified time base to ensure a precise correspondence between the sampling point sequence and the fabric micro-strain data on the time axis. The infrared hotspot imaging module was activated, and the number of scan rows matched the length of the sewing area was set. Emissivity correction was performed on the acquired infrared radiation intensity data, and a mapping relationship between radiation intensity and absolute temperature was established based on the law of thermal radiation. The radiation intensity value of each pixel was used... Calculate the absolute temperature of the corresponding pixel, where, The emissivity of the material surface. The Stefan-Boltzmann constant is... The absolute temperature is used. The calculated temperature matrix is ​​then subjected to gradient operator operations in the row and column directions, utilizing... ,in This represents the amplitude of the temperature gradient. For temperature, , Using pixel coordinates, the pixel-level temperature gradient magnitude is calculated to generate a temperature field gradient distribution characterizing the frictional heating effect of the line trace. The gradient distribution is combined with the absolute temperature matrix and encapsulated into a hot spot temperature field image frame, which is then arranged in time stamp order to form a temperature field image frame sequence.

[0050] By using frequency domain filtering and thermal radiation calculation, the acoustic waveform and infrared radiation intensity data from the previous step are transformed into a sequence of infrared hot spot temperature field image frames that can simultaneously characterize the mechanical vibration and frictional heat effect of sewing, thereby achieving precise quantification of the microscopic physical state of the sewing area.

[0051] For example, in the side seam sewing station of a yoga pant, a high-frequency acoustic signature pickup with a sampling frequency of 48000Hz is set. A Butterworth bandpass filter with a lower cutoff frequency of 180Hz and an upper cutoff frequency of 320Hz is applied to match the characteristic frequency band of the sewing machine needle bar impact. After filtering, the acoustic signature signal is inversely transformed and resampled to match the 1000Hz sampling frequency of the fabric micro-strain signal. The infrared hot spot imaging module resolution is set to 640×480 pixels, and the material surface emissivity ε is set to 0.92. In the received radiation intensity matrix, the Stefan-Boltzmann constant σ = 5.67×10⁻⁶ is used. -8 W / (m²K 4 The absolute temperature matrix was calculated, with a temperature range of 300K to 330K. Gradient operators were applied to the temperature matrix to obtain temperature gradient amplitudes ranging from 2K / mm to 5K / mm, which were then used to construct a temperature field gradient distribution image frame. The temperature field image frame was output after being synchronized with the acoustic signature data. Verification showed that this sequence can stably reflect the coupling state of local frictional heating and mechanical impact of the traces, supporting subsequent high-precision operational condition analysis through multi-source heterogeneous data fusion.

[0052] S1.4: Spatial registration and outlier removal are performed on the infrared hot spot temperature field image frame sequence. Multi-source heterogeneous fusion is performed on the cleaned fabric micro-strain feature data, acoustic waveform amplitude feature data and infrared hot spot temperature feature data to generate a multi-dimensional physical signal aggregation data package containing spatiotemporal synchronization markers.

[0053] S1.5: Perform standardized encapsulation and buffer queue writing operations on the multidimensional physical signal aggregation data packet, and continuously output a time-coherent sewing condition data stream according to a preset sampling frequency, so as to serve as the feature mapping input object for the subsequent time-series neural network model.

[0054] Step S2: The sewing condition data stream is input into a pre-trained temporal neural network model for feature mapping processing to generate a set of process labels including seam stretch tolerance, edge anchoring stability, and stitch density compatibility. Specifically, this includes: S2.1: Perform time window sliding segmentation processing on the input sewing condition data stream, and use the frame synchronization alignment algorithm to perform time domain registration on the fabric micro-strain feature data, acoustic pattern amplitude feature data and infrared hot spot temperature feature data to generate a multimodal temporal feature tensor with a unified spatiotemporal reference as the model input object.

[0055] It should be noted that the pre-trained temporal neural network model adopts a stacked structure of two-layer gated recurrent units (GRUs). Each GRU layer has 64 hidden units, which are used to extract long-short-term dependencies from the input multimodal temporal feature tensors of fabric micro-strain, high-frequency acoustic patterns, and infrared hot spots, and output a high-dimensional hidden layer state sequence. Subsequently, a multi-head attention mechanism is used to adaptively weight and fuse this sequence to enhance the feature representation of key process events. Then, it is fused with the original GRU hidden layer state by element-level weighting. The fused sequence is compressed into a fixed-length feature vector by global average pooling and mapped to a three-dimensional process semantic space through a fully connected layer. The normalized probability values ​​corresponding to elastic deformation recovery capability, edge warping resistance capability, and stitch density adaptation capability are output by the Softmax activation function. Finally, a linear interpolation algorithm is used to map each probability value to a preset physical dimension range, thereby generating a set of process labels that can be directly called by the nesting engine.

[0056] The input sewing process data stream is processed by time window sliding segmentation. The continuous sampling sequence is segmented and sliced ​​based on the preset time window width and frame shift parameter in order to maintain the balance between time domain resolution and frequency domain resolution in subsequent processing stages.

[0057] For each segmented time window frame, a frame synchronization alignment algorithm is invoked to perform time-domain registration of fabric micro-strain characteristic data, acoustic waveform amplitude characteristic data, and infrared hot spot temperature characteristic data using high-precision timestamp information, ensuring that the three types of input signals have strict time consistency at the beginning of the same frame.

[0058] During the time-domain registration process, interpolation resampling is performed based on the sampling frequency difference of the multimodal signal sources. Polynomial interpolation compensation is performed on the infrared hot spot temperature feature data with lower sampling frequency to ensure that it strictly matches the number of sampling points of the fabric micro-strain and acoustic pattern amplitude feature data.

[0059] After interpolation and resampling are completed, amplitude normalization is performed on the three types of data channels to eliminate the influence of differences in signal dimensions and amplitude ranges on the model input, and dimension consistency is achieved with a unified normalization interval of [-1,1].

[0060] Based on the above registration and normalization results, the fabric micro-strain, acoustic waveform amplitude and infrared hot spot temperature features of the same time window are spliced ​​in channel order to construct a multimodal temporal feature tensor containing all modal feature vectors, which can be used as the input object for subsequent temporal neural network models.

[0061] This chain-processing method transforms the sewing process data stream from the previous step into a multimodal temporal feature tensor with a unified spatiotemporal reference and dimensional consistency, thereby achieving high adaptability of the input data in the multimodal signal fusion and machine learning feature mapping stages.

[0062] For example, in the sewing condition data stream collected at the yoga garment sewing station, the sampling frequency of the fabric micro-strain characteristic data is 1000Hz, the sampling frequency of the acoustic signature amplitude characteristic data is 4000Hz, and the sampling frequency of the infrared hot spot temperature characteristic data is 250Hz. The time window width is set to 0.5 seconds, and the frame shift is 0.25 seconds. Within each window, the fabric micro-strain sequence is truncated to 500 points, the acoustic signature amplitude sequence to 2000 points, and the infrared hot spot temperature sequence to 125 points. Using a frame synchronization alignment algorithm, referencing the timestamp of the acoustic signature amplitude data as a benchmark, linear interpolation resampling is performed on the fabric micro-strain data to generate a time-domain sequence matching 2000 points; cubic polynomial interpolation resampling is performed on the infrared hot spot temperature data to expand the 125 points to 2000 points. Amplitude normalization is performed on the three channels respectively, uniformly mapping the fabric micro-strain voltage value, acoustic signature amplitude value, and infrared hot spot temperature value to the [-1,1] interval. The features of three channels within the same time window are concatenated in the order of [fabric surface micro-strain, acoustic waveform amplitude, and infrared hotspot temperature] to form a multimodal temporal feature tensor of shape (2000, 3). In this embodiment, the multimodal temporal feature tensor is input into a lightweight temporal neural network for feature mapping. Validation results show that the convergence speed of the model is significantly improved during the training phase, and the stability of the predicted process indicators is greatly enhanced.

[0063] S2.2: Based on the multimodal temporal feature tensor, the pre-trained temporal neural network model is called to perform forward inference calculation. The gated recurrent unit is used to extract long-short-term dependencies and perform attention mechanism weighted fusion to output a high-dimensional hidden layer state vector sequence that represents the dynamic evolution law of the sewing process.

[0064] The multimodal temporal feature tensor is subjected to forward inference operation by calling a pre-trained temporal neural network model. The feature tensor is updated step by step using a built-in gated recurrent unit structure. The activation function responses of the input gate, forget gate and output gate are used to dynamically control the feature retention and discard ratio at each time step.

[0065] The calculation of the hidden state at each time step depends on the weighted sum of the hidden state at the previous time step and the current input features. Matrix multiplication and nonlinear transformation are performed according to the state update formula of the gated recurrent unit to ensure the complete capture of long-short-term dependencies.

[0066] After the hidden state is updated, the multi-head attention mechanism is invoked to calculate the weights of the hidden states at all time steps. The original attention weight matrix is ​​generated based on the dot product similarity between the query vector and the key vector, and the matrix is ​​normalized to suppress numerical instability.

[0067] The obtained attention weight matrix is ​​multiplied with the hidden state value vector to obtain a set of context vectors with adjusted weights, thereby enhancing the features of key time steps and suppressing the features of non-key time steps.

[0068] The enhanced context vector sequence is fused with the original hidden state vector sequence using element-wise weighting. The fusion ratio is set according to the fixed hyperparameters obtained during the model training phase for the sewing conditions of yoga clothes. The output is a high-dimensional hidden state vector sequence that represents the dynamic evolution of the sewing process, thereby achieving fine-grained feature extraction of changes in sewing conditions.

[0069] By combining gated recurrent units with attention mechanisms, the unified spatiotemporal benchmark multimodal feature tensor from the previous step is transformed into high-dimensional hidden layer state data that adapts to the process semantic mapping requirements, thereby realizing the dynamic process feature input required by the nesting algorithm.

[0070] S2.3: Perform a fully connected layer mapping transformation on the high-dimensional hidden layer state vector sequence, and use a soft maximum activation function to project the hidden layer feature distribution onto the three-dimensional process semantic space to generate initial probability distribution vectors corresponding to elastic deformation recovery capability, edge warping resistance capability and stitch density adaptation capability, respectively.

[0071] The high-dimensional hidden layer state vector sequence output by the temporal neural network is processed by a fully connected layer mapping transformation. The preset weight matrix and bias vector are called to perform matrix multiplication and addition operations. The multimodal feature components of each hidden layer state vector are recombined according to the domain correlation to form a linear mapping result with a clear dimension division.

[0072] The mapping result is input into the Softmax activation function calculation unit, and the exponential function value is calculated for each process semantic dimension. The overall normalization operation ensures that the numerical range of each component in the output vector is between 0 and 1 and the sum is 1.

[0073] The following Softmax calculation formula is used in this normalization process: in For the first The probability of each process dimension For the first The mapping value corresponding to each process feature component It is a natural exponential function. For the summation index, there are 3 dimensions, and the denominator is the sum of the index values ​​of all components, thus ensuring the consistency and comparability of the probability distribution.

[0074] The normalized three-dimensional probability distribution vectors are projected onto the three process semantic axes of "elastic deformation recovery capability", "edge warping resistance capability" and "stitch density adaptation capability" respectively, and the probability distribution vectors of each dimension are retained as the initial probability distribution output objects.

[0075] By using fully connected mapping and Softmax projection, the high-dimensional hidden layer feature states of the previous step are transformed into three-dimensional probability distribution data that can be quantified to represent elastic recovery, edge anti-warping and stitch adaptation capabilities, thus realizing a direct mapping of process characteristics from hidden layer abstraction to physical semantics.

[0076] S2.4: Perform normalized scalar transformation operation based on the initial probability distribution vector, and use a linear interpolation algorithm to map the probability values ​​into process quantification indicators with clear physical dimensions, so as to generate scalars containing specific values ​​for suture extension tolerance, edge anchoring stability, and stitch density compatibility.

[0077] When performing normalized scalar transformation based on the initial probability distribution vector, three probability values—including elastic deformation recovery capability, edge warping resistance capability, and stitch density adaptation capability—are used as inputs. For each probability value, a normalization operator is called to map its relative amplitude within the [0,1] interval to a quantifiable process index with clearly defined physical dimensions, ensuring that the mapping ranges of different dimensions are consistent with the preset process benchmark. During normalization, the acquisition time window range parameter corresponding to the probability value is first read to determine the start and end boundaries of the normalization mapping. Then, a translation and scaling operation is performed based on the mean and variance of the sample distribution to generate an intermediate scalar within the target physical quantity range. Subsequently, a linear interpolation algorithm is performed on the intermediate scalar, using the interpolation formula... in, The mapped physical quantity value. Input probability values, It is the minimum value of the physical quantity dimension. To maximize the physical quantity dimension, the calculation results are limited to the physical quantity domain of the corresponding process index. Interpolation operations rely on the physical boundary parameters of each process dimension, which are obtained statistically from previous sewing experiments and embedded within the mapping function. Precision enhancement processing is performed on the interpolation results, eliminating accumulated errors in the mapping process through decimal truncation and floating-point compensation, ensuring that the generated results can be directly used for process adaptability evaluation. The mapping operations for the three dimensions output scalars for seam stretch tolerance, edge anchoring stability, and stitch density compatibility, respectively. A timestamp binding mechanism is used to attach a current calculation time marker to each scalar. Through this processing, the initial probability distribution vector from the previous step is transformed into quantifiable and verifiable process physical indicators, enabling the nesting engine to accurately respond to dynamic feedback from sewing conditions.

[0078] S2.5: Perform structured encapsulation processing on the generated scalars of stitch extension tolerance, edge anchoring stability, and stitch density compatibility. Use a key-value pair assembly protocol to bind the quantitative indicators of the three dimensions with the current timestamp to generate a set of process tags that can be directly called by the nesting engine.

[0079] like Figure 2 As shown, step S3 involves constructing a main queue based on the pre-sorted set of cut pieces in descending order of area, and establishing a secondary queue associated with the set of process labels, forming a dual-queue architecture for storing cut pieces to be arranged and real-time feedback labels. Specifically, this includes: S3.1: Obtain the pre-sorted set of cut pieces in descending order of area, and use the memory address indexing algorithm to assign a unique logical pointer to each cut piece object in the set, generating a data stream of cut pieces to be arranged with random access capability, so as to establish the basic input unit of the main queue.

[0080] The input object is a set of pattern pieces pre-sorted in descending order of area and their known geometric and material properties data. The pattern piece objects in the set have not yet been marked with logical pointers and are in a static list state.

[0081] A traversal access process is performed on the pre-sorted cut piece set in descending order of area, sequentially reading the geometric data structure and material attribute metadata of each cut piece object to establish the initial control variables for the traversal index.

[0082] During the traversal, the memory address indexing algorithm is called to allocate a unique logical pointer based on the physical storage address of the piece object in the collection. The logical pointer is then bound to the piece object using a pointer mapping table, forming an access index that is independent of the physical storage location.

[0083] For the cut piece object with bound logical pointers, perform data reorganization processing, sort the cut piece geometric outline, area value and material attribute fields in ascending order according to the logical pointers, and generate a data stream format with random access capability.

[0084] Perform cache initialization on the generated random access data stream, standardize and encapsulate the data stream according to the structured unit granularity required by the subsequent main queue, and maintain the topological order relationship of descending area.

[0085] Through the above logical pointer mapping and data stream reorganization, the pre-sorted set of cut pieces in descending order of area is transformed into a data stream of cut pieces to be arranged with random access capability, thereby establishing the basic input unit of the main queue.

[0086] S3.2: Based on the data stream of the cut pieces to be arranged, a circular buffer writing strategy is adopted to load the cut piece objects with logical pointers into the main queue in sequence, and maintain the topological order of monotonically decreasing area during the loading process to generate a main queue instance with fixed capacity and support for fast tail extraction.

[0087] Based on the logical pointer index results of the data stream of cut pieces to be arranged, the memory address space segmentation of the main queue is initialized by calling the circular buffer write strategy to ensure that the physical storage location and logical pointer pairing relationship of each cut piece object remains stable during data loading. An area value parsing operation is performed on the data stream of cut pieces to be arranged. The parsed area quantification index is used as the sorting basis, and a monotonically decreasing order maintenance algorithm is executed. This algorithm calculates the difference between the area of ​​the currently loaded cut piece and the area of ​​the cut piece at the head of the queue before writing to the queue and inserts them in an ordered manner, ensuring that the overall topological order of the queue is arranged from largest to smallest. Using a pointer-based circular buffer position management mechanism, a circular index value is assigned to the head and tail of the queue for each cut piece during the writing process, and the buffer start address is automatically wrapped around through a circular counter overflow detection to achieve a fixed-capacity circular structure. A tail-fast extraction optimization operation is performed on the inserted cut pieces. A tail index cache area is preset in the queue control block, and a direct access pointer for the tail cut piece is maintained to reduce the access latency of subsequently extracting the smallest area cut piece from the tail. By combining a queue capacity monitoring module, the occupancy rate and remaining capacity of the circular buffer are detected in real time. If the preset capacity threshold is reached after loading, a write lock is triggered to prevent overflow and maintain data consistency. Through the above processing method, the data stream of cut pieces to be arranged in the previous step is transformed into a main queue instance with fixed capacity, monotonically decreasing area topology, and support for fast tail extraction, thereby achieving efficient storage and fast access capabilities for cut piece data.

[0088] For example, in a yoga garment production scenario, a data stream of cut pieces sorted by area is obtained. Assume the cut piece areas are 0.95 square meters, 0.82 square meters, 0.73 square meters, 0.60 square meters, and 0.55 square meters respectively, with each cut piece object having a unique logical pointer. The circular buffer capacity is set to 8 cut piece units, and the circular index values ​​are initialized from 0 to 7. During loading, when the first cut piece (0.95 square meters) is written, the queue state is index 0; when the second cut piece is loaded, the area difference of 0.95 square meters is calculated. The result is 0.13 square meters, which meets the condition of monotonically decreasing. Insert index 1; calculate the area difference of 0.82 when loading the third piece. 0.73, resulting in 0.09 square meters, inserting at index 2, and so on up to index 4; in the circular buffer overflow test, when the index reaches 7, incrementing by 1 triggers a circular wrapback to index 0, achieving continuous cyclic writing; tail fast extraction optimization sets the direct access pointer for the tail piece (0.55 square meters), enabling subsequent extraction operations to be completed within a single pointer dereference, keeping the access latency within one CPU clock cycle. Verification shows that this cache queue instance significantly reduces the average access time when extracting 3–5 smallest area pieces at the tail in subsequent local reordering, greatly improving data scheduling efficiency and supporting the real-time requirements of subsequent dynamic reordering logic.

[0089] S3.3: Receive the set of process tags generated by the previous steps, which includes the tolerance of seam extension, edge anchoring stability and stitch density compatibility. Use the timestamp alignment algorithm to bind the latest tag in the set of process tags with the current sewing station status to generate a dynamic process tag data packet with real-time working condition characteristics.

[0090] S3.4: Based on the dynamic process tag data packet, construct a key-value pair mapping table and store the dynamic process tag data packet as the value field in the auxiliary queue. At the same time, establish a bidirectional reference index between the auxiliary queue and the main queue to generate an auxiliary queue instance that can reflect changes in sewing physical constraints in real time.

[0091] Based on the three scalars—seam extension tolerance, edge anchoring stability, and stitch density compatibility—included in the dynamic process label data packet, the hash mapping construction module is invoked to generate corresponding key-value pair structures within the secondary queue namespace. Each scalar is stored as a value field, and the process category identifier is used as the key field. For the aforementioned key-value pair structure, a timestamp reuse binding operation is performed, embedding the timestamp field of the dynamic process label data packet into the metadata area of ​​the key-value pair structure to ensure that the label value field in the secondary queue corresponds one-to-one with the specific instantaneous working condition. A bidirectional reference index generation algorithm is invoked, using the cut piece object with a unique logical pointer in the main queue as the index source and the dynamic process label value field with the corresponding timestamp in the secondary queue as the association target, establishing a bidirectional index matrix from the cut piece object to the process label and from the process label back to the cut piece object. Index matrix consistency verification is performed, verifying the consistency between the cut piece logical pointer and the label timestamp in all index pairs, and registering the verified index pairs to the reference control table of the secondary queue instance. By applying a memory write lock setting operation, the auxiliary queue instance is updated to a structured storage unit with the ability to bind cut pieces and process labels in real time. Through the above chain processing method, the dynamic process label data packet of the previous step is transformed into an auxiliary queue instance that can reflect changes in sewing physical constraints in real time and can be directly called by the main queue, realizing the two-layer association and dynamic scalability of the nesting data architecture.

[0092] S3.5: Perform memory synchronization verification on the main queue instance and the auxiliary queue instance to verify the integrity of the bidirectional reference index and initialize the queue read-write lock state, ultimately forming a dual-queue architecture for storing cut pieces to be arranged and real-time feedback labels and supporting concurrent read and write operations.

[0093] When performing memory synchronization verification on the main queue instance and the auxiliary queue instance, the bidirectional reference index table is used as the initial input object, and the memory address consistency detection algorithm is called to calculate the hash value of each pair of master and auxiliary pointers in the index table to generate a reference pair verification vector. The continuous matching results in the reference pair verification vector are used to call the index integrity verification module to rebind the addresses of failed matching reference pairs to eliminate invalid mappings. After binding, the concurrent queue lock state initialization program is called to set the state of the read-write locks of the main queue and auxiliary queue, making the initial state of the locks globally shared and exclusively writable. Based on the read-write lock state, the thread scheduler is called to generate a concurrent access control table, and the memory offset of this control table is embedded in the metadata area of ​​the dual-buffered queues to achieve access synchronization. After the synchronization verification is completed and the lock state initialization is finished, a dual-queue architecture instance with complete reference indexes and synchronization control metadata is generated, enabling efficient concurrent read-write association between the cut piece data to be arranged and the real-time process label data.

[0094] By employing methods such as memory address consistency detection, index table binding repair, read-write lock state initialization, and access control table attachment, the primary and secondary cache instances from the previous step are transformed into a dual-cached queue structure with data integrity and thread safety, thereby achieving the high-concurrency, secure, and real-time data retrieval technology effect of the sorting engine.

[0095] like Figure 3 As shown, step S4 involves monitoring the set of process tags in the auxiliary queue and determining whether the latest received suture extension tolerance value is lower than a preset stability threshold to trigger local reordering. Specifically, this includes: S4.1: Perform polling and reading of the process tag set stored in the auxiliary queue to obtain the latest target process tag set generated within the current time window as the initial input data.

[0096] The loop polling control logic is invoked on the memory-mapped region of the auxiliary queue instance, and a fixed sampling interval is set to correspond to the clock cycle of the embedded nesting controller to ensure time consistency during the reading process.

[0097] The queue node references returned during the polling process are sorted by timestamp priority, and the node with the largest latest timestamp field value is locked as the current candidate target tag source.

[0098] Perform a data dereference operation on the process tag set object of the locked node to extract a complete set of key-value pairs containing three-dimensional quantitative indicators of stitch extension tolerance, edge anchoring stability, and stitch density compatibility.

[0099] The key-value pair set is copied from the cache to the temporary buffer of the main control flow, and the system time of tag acquisition is recorded for timing consistency verification of subsequent judgment logic.

[0100] Set read-only access permissions for the process tag collection object in the temporary buffer to prevent data mutations caused by queue write threads during the judgment process, thereby establishing stable input conditions.

[0101] By using the polling sorting and dereference caching methods described above, the dynamic data of the auxiliary queue in the previous step is transformed into a unique set of target process tags within the current time window, thereby achieving accurate capture of the latest sewing status.

[0102] For example, in a typical yoga wear automatic nesting system, the auxiliary queue capacity is set to 512 tag nodes, the embedded nesting controller clock cycle is 5 milliseconds, and the sampling interval is 2 clock cycles (10 milliseconds). The loop polling logic calls a sorting algorithm each time it executes, sorting all nodes' timestamp fields in descending order and selecting the node with timestamp value 1679423568123 as the target tag source. This node's process tag set object contains a set of key-value pairs with a scalar values ​​of 0.42 for seam stretch tolerance, 0.78 for edge anchoring stability, and 0.65 for stitch density compatibility. This set is copied to the main control flow's temporary buffer via a dereference operation, and the current system time (1679423568135) is recorded in the buffer. Before subsequent decision-making processes, this buffer is set to read-only access to ensure data stability. Using this processing method, the system can reliably capture the latest process tags within each time window and significantly improve the accuracy and stability of action response in trigger condition calculations.

[0103] S4.2: Perform key-value parsing based on the dimensional field information contained in the target process label set to extract the numerical feature quantity of sewing stretch tolerance, which characterizes the elastic deformation recovery ability of the fabric under sewing tension.

[0104] Based on the latest target process tag set object output by sub-step S4.1 within the current time window, an ordered key-value parsing operation is performed on the internal data structure of the tag object to locate the index position of the field containing the semantics of elastic deformation recovery capability. The value domain data at this index position is read by calling the embedded parsing function, and the original value of the stitch extension tolerance stored in floating-point form is extracted as an intermediate feature quantity. Non-numerical markers are removed during the parsing process to ensure the purity of subsequent calculations. A unit consistency check algorithm is applied to the extracted intermediate feature quantity, and dimensional standardization is performed according to the physical dimensions preset by the process protocol to ensure that it is comparable under any sampling source. The signal smoothing module is called on the dimensionally standardized feature quantity, and a three-point median filtering method is used to remove isolated peaks caused by instantaneous sensor impact, making the numerical characteristics of stitch extension tolerance more stable. A numerical encapsulation operation is performed on the smoothed feature quantity, associating it with the current timestamp and tag ID through key-value binding to generate a stitch extension tolerance numerical feature quantity data package that can be directly called by sub-step S4.3. Through the above processing method, the semantic field of elastic deformation recovery capability in the target process label set is transformed into a cleaned, standardized and stabilized numerical feature of suture extension tolerance, so as to achieve the effect of dynamic threshold comparison preparation based on real-time process labels.

[0105] For example, at the A3 sewing station of the yoga garment production line, the latest tag in the auxiliary queue has a sewing stretch tolerance field index of 7 and a value range of 0.842 (the unit is the relative stretch ratio expressed in meters / meters). During the parsing process, any possible unit symbols and punctuation are first removed, extracting the original value as 0.842. Then, based on the preset process protocol dimensions, the relative stretch ratio in meters / meters is mapped to a dimensionless floating-point value. The dimensionally standardized feature quantity is processed in a three-point median filter using a window sequence [0.842, 0.846, 0.841], resulting in an output of 0.842, significantly reducing the impact of noise. This value is then bound to the timestamp 1689573258 and the tag ID "TAG_A3_0725" through key-value mapping, forming a structured data packet {"stretch tolerance": 0.842, "timestamp": 1689573258, "tag ID": "TAG_A3_0725"}. This data packet can be directly used in threshold comparison calculations in S4.3. Experiments show that the stability of the feature quantity after the above processing is greatly improved in multiple batches of production. It can accurately convey the true recovery ability of the fabric under sewing tension conditions, providing a reliable data basis for subsequent dynamic reordering trigger judgment.

[0106] S4.3: Utilize the process stability threshold parameter fixed inside the embedded nesting controller to perform boundary comparison operation on the numerical characteristic of the suture extension tolerance, so as to generate a deviation judgment flag.

[0107] The numerical characteristic of the suture extension tolerance transmitted from S4.2 calls the process stability threshold parameter reading interface that is fixed inside the embedded nesting controller, loads the standard process stability boundary value stored in the read-only memory area, and generates a threshold data instance that can participate in the comparison operation in the internal register.

[0108] The numerical feature of the suture extension tolerance and the threshold data instance are processed in the same arithmetic unit to ensure that their physical dimensions and numerical precision are consistent, and the difference in calculation precision is eliminated by double-precision floating-point format conversion.

[0109] During the comparison operation phase, the boundary judgment module is invoked to perform numerical difference calculation, and the deviation is generated using the absolute error measurement method. The formula for calculating the deviation is as follows: in This is the deviation amount. This is a numerical characteristic of suture stretch tolerance. This is the threshold parameter for process stability.

[0110] Perform sign analysis on the deviation. If the sign is negative, it indicates that the current suture extension tolerance is below the safety boundary, triggering a status bit update. If the sign is zero or positive, the safety status flag is maintained.

[0111] The status bit and the deviation amount are encapsulated together into a deviation judgment flag data structure, which is written through the shared memory area of ​​the controller's internal high-speed cache, so that the main control process can read the judgment result in real time during uninterrupted sorting execution.

[0112] By using the above boundary comparison and status identification processing method, the stitch extension tolerance feature value parsed in the previous step is transformed into a deviation judgment flag that characterizes whether the current sewing condition exceeds the safety boundary, thereby achieving the condition judgment technology effect of dynamically triggering the reordering protocol.

[0113] For example, in the sewing station of a yoga wear production line, the embedded pattern layout controller has a fixed process stability threshold parameter set to an elongation tolerance value of 1.85 mm. The collected sewing elongation tolerance value, after A / D conversion and standardization, is 1.62 mm. The threshold parameter T=1.85 is loaded, uniformly formatted as double-precision floating point, and deviation calculation is performed, resulting in Δ=-0.23 mm, with a negative sign, and the status bit is updated to "out of bounds". The deviation judgment flag data structure includes the deviation value -0.23, the status bit "out of bounds", and the current timestamp. After reading this flag, the production control system triggers a local reordering, replacing some pieces in the pattern layout sequence to adapt to the current low elongation tolerance condition, thereby significantly improving material utilization and avoiding sewing defects.

[0114] S4.4: Perform conditional branch jump processing based on the logical state of the deviation determination flag to generate the local reordering used to indicate whether to start or maintain the current nesting process.

[0115] Based on the logical state of the deviation judgment flag, the conditional branch compilation unit inside the embedded nesting controller is invoked to perform Boolean value mapping on the judgment flag to form a binary condition variable that can be used for hardware execution flow. This binary condition variable is loaded into the controller's branch jump register, triggering the hardware-level pipeline instruction scheduling module to perform branch prediction and path preloading to reduce execution latency when triggering the protocol. A bitwise AND operation is performed on the condition variable in the branch jump register with a preset reordering mode mask to obtain a valid condition signal for determining whether to enter the reordering path. The condition signal drives the instruction generation unit to extract the corresponding reordering mode start instruction or static nesting maintenance instruction from the protocol trigger instruction index table using a lookup table, and generates a structured control signal packet for subsequent execution unit invocation. Timestamp binding and context association operations are performed on the generated control signal packet, associating the signal packet with the current nesting process state dataset to ensure that the signal remains consistent with the process state during scheduling. Through the above conditional branch jump processing method, the deviation judgment flag is transformed into a specific local reordering operation, achieving hardware-level fast mapping from the judgment result to the execution action and branch path optimization.

[0116] For example, in a yoga wear production line, when the seam stretch tolerance value is 2.35 and the process stability threshold is fixed at 3.00, the deviation judgment flag is set to 1. The embedded nesting controller maps the logic value 1 to the binary condition variable 01. After loading the branch jump register, the pipeline scheduling module preloads the protocol instruction template required for reordering the path. The branch condition variable and the pattern mask... After the bitwise AND operation, the valid condition signal is output. The instruction generation unit retrieves the reordering mode startup instruction code "RS_INIT" from the index table and encapsulates it into a control signal packet, binding the current layout batch's status context ID to "CTX_202404". This signal packet is delivered to the execution unit within 200 milliseconds, directly switching the layout engine to dynamic local reordering mode. Verification results show that material utilization is significantly improved, fabric edge curling defects after cutting are greatly reduced, overall sewing stability meets process standards, and the output finished fabric meets quality inspection requirements.

[0117] S4.5: Update the execution context of the main control flow based on the state of the local reordering to complete the seamless switching control from static nesting mode to dynamic local reordering mode.

[0118] Step S5: If a local reordering is triggered, multiple smallest area cut pieces that have not yet been arranged are extracted from the tail of the main queue to form a candidate cut piece set, and an alternative cut piece set is selected based on the current edge anchoring stability level. Specifically, this includes: S5.1: Perform reverse index traversal processing on the data stream of cut pieces to be arranged at the tail of the main queue, and use the area priority extraction algorithm to lock multiple minimum area cut piece objects that have not yet been arranged, so as to generate a basic set of candidate cut pieces containing low material usage characteristics.

[0119] When performing reverse index traversal processing on the data stream of cut pieces to be arranged at the tail of the main queue, a reverse iterator is established starting from the tail element in the current queue state, and the logical pointer mapping table is called to locate the cut piece objects that have not yet been marked as arranged. Using an area priority extraction algorithm, the two-dimensional geometric area parameters of each cut piece object are used as the sorting basis, and a priority sorting operation is performed from smallest to largest. The extraction range is limited to a preset depth window at the tail by combining the iterator index position. Based on the sorting results, cut piece objects with area parameters greater than a dynamic threshold are removed by a filter. This dynamic threshold is calculated by multiplying the area of ​​the smallest bounding rectangle of the remaining gap cluster in the current layout by a safety redundancy coefficient.

[0120] After filtering, the set of cut pieces is written to the basic candidate cut piece set cache unit in ascending order of area. During the writing process, the material attribute index code and geometric envelope descriptor of each cut piece are bound to facilitate subsequent rigid matching and topology adaptation calculations. By using reverse indexing and area priority extraction, the data stream at the tail of the queue in the previous step is transformed into a data set with low material consumption characteristics that supports subsequent process adaptation matching, realizing the basic resource pre-selection function for local reordering.

[0121] S5.2: Based on the basic candidate cut piece set, read the latest generated process tag set in the auxiliary queue, and use the key value parsing protocol to extract the edge anchoring stability scalar that characterizes the anti-curling ability of the cut piece edge under sewing traction, so as to obtain the dynamic rigid constraint threshold under the current sewing conditions.

[0122] Based on the logical pointer index result of the basic candidate cut pieces set, the latest data object of the auxiliary queue is called to perform a memory read operation to obtain a set of process labels containing three-dimensional indicators of seam stretch tolerance, edge anchoring stability and stitch density compatibility.

[0123] The key-value pair mapping parsing process is performed on the read process tag set to lock the numerical field of the corresponding edge anchoring stability dimension in the key-value domain, and the unit dimension is ensured to be consistent with the physical meaning during the parsing process to avoid attribute confusion.

[0124] The extracted edge anchoring stability scalar is input into the dynamic constraint threshold calculation module. This module performs a threshold fine-tuning algorithm based on the comprehensive signal characteristics of the current sewing conditions, and generates a dynamic rigid constraint threshold by combining the edge anchoring stability base value and the working condition coefficient.

[0125] In the calculation of dynamic constraint thresholds, the historical edge anchoring stability data is filtered using the exponential smoothing method. The smoothing coefficient depends on the frequency of operating condition fluctuations, and the threshold is updated using the following formula: in, This is the current dynamic rigid constraint threshold. For smoothing coefficients, The threshold value at the previous time step. This represents the current extracted edge anchoring stability value.

[0126] The calculated dynamic rigid constraint threshold is bound to the basic candidate pattern set to form a pattern constraint description object with real-time working condition adaptability attributes, providing physical boundary conditions for subsequent multi-dimensional feature matching operations. Through the calculation and binding of the dynamic constraint threshold, the basic pattern set from the previous step is transformed into an adaptable dataset containing rigid constraint information under the current sewing conditions, realizing real-time constraint matching in the pattern selection process.

[0127] For example, in the production process of yoga clothing, when the basic candidate pattern set contains 5 pattern objects with an area between 25cm² and 40cm², the edge anchoring stability scalar in the latest label of the auxiliary queue is 72.5N / m, the historical average is 70.0N / m, and the smoothing coefficient α corresponding to the working condition fluctuation frequency is set to 0.6, the dynamic rigid constraint threshold is calculated as follows: The threshold was determined to be 71.0 N / m. This threshold was bound to the constraint description of the above 5 pattern pieces. In the subsequent feature matching, pattern pieces with edge anchoring stability lower than 71.0 N / m were removed, and finally 3 candidate pattern pieces that meet the high edge anchoring stability requirements were generated, which effectively improved the edge anti-curling performance and significantly improved the sewing stability.

[0128] S5.3: For each piece object in the basic candidate piece set, call the pre-stored material mechanical property mapping table, perform multi-dimensional feature matching operation in combination with the dynamic rigidity constraint threshold, and use the edge anchoring stability evaluation model to calculate the deformation resistance score of each piece under the current stitch density, so as to generate a candidate piece feature vector sequence with quantified stiffness index.

[0129] Based on the selected fabric pieces in the basic candidate fabric piece set, a pre-stored material mechanics property mapping table is read and loaded to establish a mapping relationship between fabric piece IDs and material parameter sets, including Young's modulus, Poisson's ratio, fabric weight, fiber orientation coefficient, and edge coating thickness. The geometric parameters of the fabric pieces are indexed and bound to the aforementioned material parameter sets to form a callable material mechanics input set. The dynamic rigidity constraint threshold, fixed in the process database, is used as the reference boundary condition for the edge anchoring stability assessment model, configuring the model's internal deformation resistance calculation process. Multi-dimensional feature matching is performed on each fabric piece to calculate the transverse bending stiffness, longitudinal tensile stiffness, and edge curling critical stress under the current stitch density, using normalized vector combinations to form the deformation resistance scoring input matrix. The deformation resistance scoring formula is constructed and calculated; for example, the evaluation model output score can be implemented as follows: in, For Young's modulus, Let the moment of inertia of the cross section be... The critical stress for edge curling. This is a correction term for line density. The deformation resistance score results of each piece are sequentially integrated to output a sequence of candidate piece feature vectors with quantified stiffness indices, which are then used in subsequent screening stages. Through the aforementioned material property mapping and evaluation model calculation process, the basic candidate piece data from the previous step is transformed into candidate piece feature vectors containing quantified deformation resistance indices, achieving data-driven edge anchoring stability screening preparation.

[0130] For example, in the actual scenario of yoga garment production, the basic set of candidate pattern pieces contains 5 unarranged pattern pieces, each corresponding to an E value of 8.5 × 10 in the material mechanical property mapping table. 9 Pa and I values ​​are 1.2 × 10 -9 m 4 The value of σ is 3.4 × 10 6 Pa and δ are both 0.15. The dynamic rigid constraint threshold is set to a score of no less than 2.0. Substituting the above parameters into the formula... The evaluation model output score was 2.999, which, after normalization, formed a deformation resistance score matrix [2.999, 2.850, 2.100, 1.900, 2.450]. Pieces with scores higher than 2.0 will proceed to the next screening stage, filtering out those with insufficient deformation resistance. This processing significantly improved the edge anchoring stability matching degree during actual piece replacement, enhancing the dynamic adaptability of the pattern layout to sewing conditions.

[0131] S5.4: Based on the candidate pattern feature vector sequence, perform threshold comparison and logical filtering operations, and use Boolean mask filtering mechanism to remove low-rigidity pattern pieces with edge anchoring stability scores lower than the preset safety boundary, so as to output a set of high-quality alternative pattern pieces that only contain high edge anchoring stability adaptation conditions.

[0132] The logical filtering module is called to load the preset high edge anchoring stability safety boundary value for the candidate pattern feature vector sequence. The edge anchoring stability score of each pattern in the sequence is compared with the threshold value. The score value and the safety boundary value are calculated by the unit level difference to generate the deviation state matrix.

[0133] A Boolean mask generation mechanism is applied to the deviation state matrix. Cutting pieces with scores below the safety boundary are marked as mask failure bits, and cutting pieces with scores above or equal to the safety boundary are marked as mask retention bits, so as to form a mask control vector with binary logic states.

[0134] The candidate pattern piece feature vector sequence is filtered bitwise using a mask control vector. During the filtering process, pattern piece data objects that meet the high edge anchoring stability adaptation condition are retained, all low-rigidity pattern piece data objects are removed, and a high-quality alternative pattern piece feature vector sequence containing only valid pattern pieces is generated.

[0135] The sequence reorganization and index update processes are performed on the high-quality alternative cut feature vector sequence to maintain its continuity and index integrity in the storage structure, so that subsequent memory address remapping and queue state update operations can be directly called based on the stable index.

[0136] By using the above-mentioned logical screening and mask filtering methods, invalid cut data in the candidate cut feature vector sequence are transformed into a high-quality alternative cut set containing only high edge anchoring stability adaptation conditions, thereby achieving a significant improvement in resource quality under local reordering.

[0137] For example, for 20 cut-piece objects in the candidate cut-piece feature vector sequence, the edge anchoring stability score ranges from 0.35 to 0.92, and the high-rigidity safety boundary value is set to 0.65. The logic filtering module is called to perform the score difference calculation, specifically: in The difference. The score for the cut pieces, The safety boundary value is 0.65. The difference... The mapping is done using a Boolean mask. A score below 0.65 corresponds to a null mask bit of 0, while a score above or equal to 0.65 corresponds to a null mask bit of 1. For example, a piece with a score of 0.72 has a difference Δ = 0.07, and its mapped mask value is 1. After bitwise filtering, only 12 pieces with scores between 0.65 and 0.92 remain in the sequence, generating a 12×M dimensional feature vector sequence of high-quality replacement pieces (M is the feature dimension). After sequence reorganization and index update, these 12 pieces form a continuous index block of 2021 to 2032 in memory, facilitating subsequent position replacement algorithms. The output results, verified by topological matching, show that the anti-curling performance of the high-rigidity pieces is significantly improved under the current working conditions, ensuring higher process stability in the sewing process after local reordering of the layout sequence.

[0138] S5.5: Perform memory address remapping and queue state update processing on the set of high-quality alternative cut pieces, and use the pointer swapping algorithm to mark the selected high-rigidity cut pieces as replaceable resource units to complete the preparation of replacement target resources for local reordering, so that they can be directly called by the subsequent topology position replacement algorithm.

[0139] Step S6: Search for gap clusters within the non-critical stress zones of the already arranged layout that can accommodate the replacement piece set, swap the positions of the replacement piece set and the original pieces in the gap clusters, and adjust the geometric arrangement to generate a pattern sequence. Specifically, this includes: S6.1: Perform polygon Boolean operations on the coordinate set of non-critical stress zones in the already arranged layout to extract the geometric envelope data of candidate void clusters with complete closed boundaries, providing basic geometric units for subsequent spatial matching.

[0140] Perform data deconstruction on the input set of coordinates of non-critical stress zones in the layout, group each coordinate point into several closed polygon candidate area objects according to the topological connectivity, and ensure that each candidate area object has a continuous boundary index chain.

[0141] The polygon Boolean operation module is called on the above candidate area objects to perform difference calculation based on the main pattern piece outline set constructed in the layout global coordinate space, and to remove coordinate points that overlap with the area occupied by the already arranged pattern pieces.

[0142] Perform boundary closure checks on the coordinate set obtained by the difference operation, verify the closure of the boundary chain at both ends using the path connectivity algorithm, and mark the closure state as a valid geometric envelope condition.

[0143] The convex hull generation algorithm is invoked on the coordinate set under the effective geometric envelope condition to generate polygonal envelope data that accurately describes the spatial range of the gap cluster, and the boundary point sequence of each gap cluster is bound to the corresponding boundary length and envelope area attribute value.

[0144] The generated polygonal envelope data is subjected to geometric feature standardization processing to unify the order and orientation of boundary vertices and the coordinate reference system, so as to form candidate gap cluster geometric envelope data that can be used for subsequent spatial matching calculations.

[0145] By using the above-mentioned polygon Boolean operations and geometric envelope construction processing method, the alternative cut piece set matching target selected in the previous step is transformed into a precise spatial description of the gap cluster, realizing the local layout space preprocessing effect based on non-critical stress areas.

[0146] For example, in the layout of yoga garment patterns, the coordinate set of non-critical stress areas is defined as the set of boundary points between the waistband fold area and the side seam allowance area. The waistband fold area contains 24 vertices, and the side seam allowance area contains 18 vertices. The polygon Boolean operation module is called to perform a difference calculation between the waistband fold area and the main pattern set, obtaining the coordinate set after removing overlapping areas. In the difference result, the waistband fold area forms a closed envelope with a boundary length of approximately 154 mm and an area of ​​approximately 2300 mm²; the side seam allowance area forms a closed envelope with a boundary length of approximately 128 mm and an area of ​​approximately 1850 mm². The convex hull generation algorithm is called to arrange the vertices of the above closed areas in a counter-clockwise order and generate geometric envelope data. The coordinate system of the geometric envelope data is unified so that it coincides with the origin of the global layout coordinate system, completing the standardization process. The resulting polygon envelope data can then directly enter the next step of the minimum bounding rectangle calculation process, ensuring that the spatial matching of the alternative pattern pieces has an accurate geometric basis.

[0147] S6.2: Calculate the minimum bounding rectangle feature vector based on the geometric envelope data of the candidate gap cluster, and project the outline of each piece in the alternative piece set to the same feature space to generate a piece-gap mapping matrix for spatial adaptation comparison.

[0148] Based on the extracted candidate void cluster geometric envelope data, the geometric feature analysis module is called to calculate the minimum bounding rectangle feature vector of each void cluster. The feature vector contains length, width, boundary coordinates and rotation angle information to provide a unified spatial reference benchmark.

[0149] The outline data of each piece in the alternative piece set is subjected to coordinate system transformation and rotation normalization according to the rotation angle parameter of the candidate gap cluster, so that pieces with different geometric shapes can be compared in the same feature space.

[0150] The bounding rectangle calculation is performed on the normalized pattern outline to extract the length, width and boundary coordinates of the pattern, forming a data structure consistent with the feature vector of the gap cluster.

[0151] A matrix mapping construction method is used to combine the feature vector of the bounding rectangle of each piece with the feature vector of the bounding rectangle of each gap cluster to generate a spatial fit comparison matrix, the matrix elements of which are fit scores.

[0152] The matching relationship between the cut pieces and the gap clusters is calculated using the fit rating matrix. The rating formula can be expressed as: in, and These are the length and width of the bounding rectangle of the cut piece, respectively. and These are the length and width of the circumscribed rectangle of the gap cluster, respectively. and The function is used to extract the minimum or maximum value of the corresponding dimension.

[0153] The results of the fit score matrix calculation are used to transform the geometric envelope data of the candidate gap clusters from the previous step into a mapping matrix that accurately matches the position of the cut pieces, enabling subsequent topological constraint screening and path optimization. Through this processing method, the geometric feature data from the previous step is transformed into a spatially computable matrix structure, achieving quantitative output of the size fit between the cut pieces and gap clusters during local reordering.

[0154] For example, in a non-critical stress area at the waist of a yoga top, three candidate gap clusters were extracted. Their geometric envelopes are closed polygons with rotation angles of 5°, 15°, and 0°, respectively, and their minimum bounding rectangle dimensions are (120mm × 80mm), (130mm × 85mm), and (100mm × 75mm), respectively. An alternative pattern set contains two pattern pieces with high edge anchoring stability, whose un-rotated bounding rectangle dimensions are (118mm × 78mm) and (95mm × 70mm), respectively. After performing rotation normalization on the candidate gap clusters and pattern pieces, the fit score between pattern piece 1 and gap cluster 1 is calculated. According to the above formula, the result is numerically calculated as (118 / 120) × (78 / 80) = 0.975 × 0.975 = 0.9506, which significantly improves the matching index. The score for piece 2 and void cluster 3 is (95 / 100)×(70 / 75)=0.95×0.9333=0.8866, which is lower than the preset fit threshold of 0.90. Therefore, it is rejected in the subsequent screening. Through this score matching, the piece-void mapping matrix is ​​finally output for subsequent topology screening, achieving the best fit match between the alternative piece and the void cluster, significantly improving material utilization and layout stability.

[0155] S6.3: Execute the topological constraint filtering algorithm using the cut-gap mapping matrix to eliminate invalid matching pairs that do not meet the edge anchoring stability level requirements, so as to output a list of feasible permutation positions that meet the high edge anchoring stability adaptation conditions.

[0156] When performing a topological constraint filtering algorithm on the piece-gap mapping matrix, the input mapping matrix is ​​used as the initial matching data set, and the envelope parameters in the matrix elements and the piece physical characteristic vectors are used as the matching criteria. For each matching pair, edge anchoring stability level analysis is performed, extracting the edge anchoring stability score scalar of the current piece object at the corresponding position in the mapping matrix, and comparing it with a preset stability level threshold. Matching pairs that do not meet the stability level threshold are indexed and marked, and all elements corresponding to these matching pairs are removed through matrix row and column shearing operations, forming an intermediate mapping matrix instance after removing invalid data. A connectivity check is performed on the intermediate mapping matrix to ensure that the remaining matching pairs meet the topological connectivity requirements within the geometric envelope boundary of the gap cluster, and position parameter updates are performed on matching pairs that achieve automatic topological repair. An index compression operation is performed on the set of matching pairs that meet the edge anchoring stability level requirements and have qualified topological connectivity, generating a list of feasible permutation positions and providing input for subsequent permutation path graph construction. Through the above filtering and matrix processing methods, the patch-gap mapping matrix of the previous step is transformed into feasible permutation position data that only contains high edge anchoring stability adaptation conditions and satisfies topological constraints, so as to realize the accurate usability of spatial matching results in the local reordering process.

[0157] For example, in yoga garment production pattern making, the input pattern-gap mapping matrix contains matching data for 12 pattern pieces and 8 candidate gap clusters. Each matching pair is accompanied by an edge anchoring stability score, ranging from 0 to 1, with a preset stability level threshold of 0.75. Matching pairs with scores below 0.75 are removed; for example, pattern piece C5 and gap cluster V2, with a score of 0.68, are removed. After the removal operation, the matrix retains 65 matching pairs. Connectivity checks reveal that 3 of these pairs cross the geometric boundaries of gap clusters and are therefore removed. The remaining 62 matching pairs all meet the topological requirements. Index compression is performed on these matching pairs to obtain a position list of length 62, providing a basis for subsequent path graph construction. The score comparison operation can be modeled as follows: in The edge anchoring stability score is used to match the cut pieces. This screening process generates a list of feasible replacement locations that significantly improves the success rate of local reordering and matching in practical applications. It also ensures that the replaced cut pieces maintain edge stability during sewing, thereby improving overall material utilization and finished product quality.

[0158] S6.4: Construct a weighted directed permutation path graph based on the list of feasible permutation positions, and apply a heuristic search strategy to traverse the weighted directed permutation path graph to determine the optimal piece insertion sequence that maximizes the overall material utilization gain.

[0159] Based on the coordinate data in the feasible replacement location list and the geometric envelope features of the alternative fabric pieces set, the path construction module is invoked to initialize the node set of the weighted directed replacement path graph and establish directed edge relationships between nodes. The weights are set based on a weighted combination of the predicted material utilization gain and the improvement in edge anchoring stability of the fabric pieces. During the weight calculation process, a parameterized scoring function maps the predicted material utilization gain and the improvement in edge anchoring stability to a unified standardized interval. The scoring function takes the following form: in, This is the predicted value of material utilization gain. This is the value that improves edge anchoring stability. and The weighting coefficients are preset according to the process priority strategy. Based on the aforementioned weighted directed permutation path graph, a heuristic search strategy is executed to initialize the search queue, prioritizing candidate paths in descending order of weight to ensure optimal resource exploration. The heuristic evaluation function uses the product of the square of the predicted material utilization rate and the cumulative path stability score as a comprehensive judgment criterion. The evaluation function has the following form: in As a heuristic evaluation value, this evaluation function is used to dynamically adjust the path expansion priority during the search process, and a boundary pruning algorithm is combined to remove branches where the material utilization gain or stability improvement value is lower than the safety threshold. By continuously traversing and recording all path sets that satisfy the two-parameter constraints, the path comparison module is called to calculate the cumulative material utilization gain value of each path. The cumulative calculation formula is as follows: in This is the cumulative gain value. This is the sequence number of the replacement operation step. For the first The gain prediction value of the step, For the first The stability improvement value of the step is calculated. The path with the highest cumulative material utilization gain value is selected and parsed into the optimal piece insertion sequence, outputting structured sequence data containing the piece ID order and insertion position index. Through weighted path construction and heuristic search strategies, the feasible replacement position list of the previous step is transformed into the optimal piece insertion sequence data, realizing dynamic global optimization of the piece geometric arrangement.

[0160] For example, in a yoga garment production batch, the alternative cut piece set contains 5 cut pieces with high edge anchoring stability. The predicted material utilization gain M ranges from 2.4 to 3.1, the edge anchoring stability enhancement value S ranges from 1.2 to 1.6, the weight coefficient α is set to 0.65, and β is set to 0.35. The optimal node weight result obtained by weight calculation is 2.515. When M=3.1 and S=1.6 in the heuristic evaluation function, the result is 15.376, which significantly prioritizes the expansion of this path in the search iteration. In the calculation of the cumulative material utilization gain value, the parameters (M+S) of the three permutations are 4.7, 4.3, and 4.6 respectively, and the formula calculation result is 13.6, which is significantly higher than other paths. The final output insertion sequence achieves a significant improvement in the utilization efficiency of the gaps between cut pieces, a significant improvement in the stability of the layout, and optimization of the continuity of the cutting path during the verification stage.

[0161] S6.5: Based on the optimal pattern insertion sequence, perform an in-situ update operation on the pattern coordinate data in the original pattern layout to generate a locally reordered pattern sequence containing dynamically adjusted geometric layout logic.

[0162] The optimal insert sequence is processed by traversing the index and calling the logical pointer mapping table of the corresponding insert in the already arranged layout coordinate matrix to locate the geometric position unit that needs to be updated.

[0163] The coordinate data replacement operation is performed on the located geometric position unit. The vector offset calculation method is used to adjust the reference point of the inserted piece to the reference alignment position of the geometric envelope of the gap cluster, and the insertion position is precisely quantized.

[0164] Perform a topology consistency check on the replaced pattern set, and use polygon wrapping rules to ensure that there is no intersection or overlap between the inserted pattern outline and the adjacent pattern in the layout.

[0165] Based on the topology consistency verification result, the local geometric constraint reconstruction module is called to perform a spline curve smoothing fit on the boundary point sequence at the connection between the inserted pattern outline and the adjacent pattern to maintain the overall geometric continuity of the layout.

[0166] The smoothed and fitted insert coordinate data is written back to the original layout file buffer. A synchronous write strategy is used to ensure the real-time visibility of the insert position update, and a two-way binding reference between the insert ID and the process label set is maintained during the update process.

[0167] By using in-situ coordinate updates, topological consistency checks, and smooth fitting, the optimal pattern insertion sequence from the previous step is transformed into a locally reordered pattern sequence that includes dynamically adjusted geometric layout logic, thus achieving a target layout that balances material utilization and process stability.

[0168] For example, in an automated pattern making process for yoga wear fabric, the optimal pattern insertion sequence includes five pattern pieces with high edge anchoring stability. The reference point coordinates of their outline rectangle envelopes are (125.4, 88.2), (98.7, 140.5), (210.0, 75.3), (180.2, 120.6), and (155.0, 95.8), respectively. The reference alignment position of the gap clusters in the already laid-out layout is defined using a Cartesian coordinate system, and the insertion coordinate offset is calculated using the following formula: in, The coordinate vector of the reference point for the cut piece. The reference coordinate vector for the void cluster. This is the offset vector. In this example, the offset vector for the first piece is (125.4). 120.0, 88.2 (85.0) yields (5.4, 3.2), which is used as the insertion position adjustment. Topological consistency verification employs a polygon wrapping rule, where the outline of each inserted piece is defined by a set of closed points. Smooth fitting uses cubic spline curves to generate smooth curve segments at the intersection of the inserted piece boundary and the boundary of adjacent pieces, ensuring boundary continuity. After this process, the geometric layout of the locally reordered pattern sequence achieves the preset material utilization gain target, and maintains the edge curling suppression effect during subsequent cutting, significantly improving the sewing stability of the finished product.

[0169] Step S7: Based on the replaced piece identifier, insertion position coordinates, and gap cluster envelope parameters of the layout sequence, construct a reordering operation log containing label matching improvement values ​​and embed it into the final layout image file header. Specifically, this includes: S7.1: Based on the results of this local reordering execution, obtain the set of replaced clip identifiers, the coordinate matrix of the newly inserted position, and the envelope parameters of the gap cluster. Use the data serialization algorithm to standardize and encode the above multi-source heterogeneous data to generate the original reordering event data stream containing the complete permutation topology.

[0170] The input object contains the set of replaced clip identifiers generated by step S6, the coordinate matrix of the newly inserted position, and the gap cluster envelope parameters, all of which are multi-source heterogeneous data structures.

[0171] The set of cut piece identifiers to be replaced is input into the identifier parsing module, which performs unique identifier format verification, removes illegal characters, and reconstructs the index sequence according to internal encoding rules to ensure the consistency of subsequent association mapping.

[0172] The newly inserted position coordinate matrix is ​​input into the geometric data standardization module. Based on the global coordinate reference of the layout, the unit conversion and decimal precision truncation of each coordinate element are performed to generate a two-dimensional coordinate array with a unified measurement system.

[0173] The void cluster envelope parameters are input into the spatial structure encoding module. The coordinate points are rearranged using the boundary vertex sequence recombination algorithm, and closure verification is performed to ensure that the geometric envelope data is complete and free of redundant nodes.

[0174] A data serialization algorithm is used to group and nest the processed index sequence, two-dimensional coordinate array, and geometric envelope data into key-value pair mapping relationships, and generate the original reordering event data stream using a structured encoding format (such as JSON or binary protocol buffer).

[0175] During serialization, a compression encoder is invoked for each coordinate element in the coordinate matrix to perform bit-width optimization, thereby reducing the bandwidth requirements for data packet transmission while maintaining lossless accuracy.

[0176] Through the serialization process described above, the local reordering result of the previous step is transformed into a raw reordering event data stream containing complete permutation topology relationships, thus enabling standardized data input for subsequent process label matching degree calculation and operation log construction.

[0177] For example, in a yoga garment pattern optimization scenario, the replacement piece identifier set contains three unique IDs: “CP_A12”, “CP_F09”, and “CP_K07”. The new insertion position coordinate matrix is ​​[[125.500,233.750],[540.000,420.375],[311.250,198.125]], in millimeters. The gap cluster envelope parameter contains four vertex coordinates in the following order: [[100.000,200.000],[150.000,200.000],[150.000,250.000],[100.000,250.000]]. The identifier resolution module converts the three piece IDs into an internal hash index [30721,48219,59108]. The geometric data standardization module preserves the coordinate matrix to three decimal places on a millimeter basis and verifies that all values ​​are within the cutting bed's working area. The gap cluster data retains its original vertex sequence after closure verification. The serialization algorithm embeds the index array, coordinate matrix, and envelope parameters into a unified key-value structure, encoding them into binary data packets with a total length of 256 bytes. The bit-width optimizer uses 16-bit fixed-point numbers to represent the coordinate elements, significantly reducing transmission latency. The output original reordering event data stream is directly referenced in subsequent process evaluation stages to calculate the matching degree improvement value, resulting in a significant improvement in optimization performance.

[0178] S7.2: Based on the change in edge anchoring stability level in the original reordering event data stream, call the process gain evaluation model to calculate the difference in stitch extension tolerance before and after reordering, so as to quantify the label matching degree improvement value scalar characterizing the optimization magnitude of the nesting scheme.

[0179] Based on the edge anchoring stability level changes contained in the generated original reordering event data stream, a difference operation is performed on these changes to obtain the absolute difference index of the levels before and after reordering. The process gain evaluation model is invoked, and the above difference index is passed as an input variable to the model's evaluation unit. Combined with the scalar history sequence of suture extension tolerance synchronously recorded in the reordering event data stream, a difference analysis window is established to identify the process adaptability differences before and after reordering. Time series segmentation is performed on the suture extension tolerance history sequence in the difference analysis window, extracting the scalar values ​​of the latest valid data point before reordering and the first stable data point after reordering. Based on the extracted data points, the suture extension tolerance difference calculation formula is applied: in, This represents a scalar value indicating the tolerance for suture stretching. This represents the measurement value after the reordering process. This represents the measurement value before the reordering was performed. The difference above... The basic quantitative indicator for improving label matching accuracy is input into the gain conversion module of the process gain evaluation model. The normalization and weighted calculation algorithms within the gain conversion module are invoked to normalize ΔCET and perform weighted calculations based on the change in edge anchoring stability level to obtain the final scalar value for improving label matching accuracy. This processing method transforms the permutation topology relationship and process physical quantity change data from the previous step into a label matching accuracy improvement value that can directly characterize the optimization magnitude of the sorting scheme, thus achieving a quantitative evaluation of the re-sorting effect.

[0180] For example, in an automated nesting task for yoga garment fabric, the original reordering event data stream recorded an improvement in edge anchoring stability from 3.2 to 3.8. The seam stretch tolerance was measured at 0.012 m / m before reordering and at 0.015 m / m after reordering. The difference calculation formula yielded ΔCET = 0.003 m / m, which was used as a basic quantitative indicator input into the process gain evaluation model. The gain conversion module set the normalization interval to 0–0.005 m / m, and the weighting coefficient was automatically set to 1.2 based on the change in stability level. After normalization, ΔCET was 0.6, and the final label matching improvement value was calculated to be 0.72 after weighted calculation. This improvement value was directly used in subsequent process audit records to evaluate the extent of optimization of this nesting scheme, and the insertion of high edge anchoring stability cut pieces effectively improved seam stretch stability during actual cutting, significantly improving the overall efficiency of the cutting and sewing processes.

[0181] S7.3: Based on the original reordering event data stream and the tag matching improvement value scalar, a structured reordering operation log object containing timestamps, operation types and multi-dimensional feature vectors is constructed using a key-value pair mapping mechanism to form a standard process audit record with machine readability.

[0182] Based on the original reordering event data stream and the tag matching degree boosting value scalar, a key-value pair mapping mechanism is invoked to achieve structured encapsulation of multi-source heterogeneous data. The set of replaced clip identifiers, the newly inserted position coordinate matrix, and the gap cluster envelope parameters contained in the original reordering event data stream are mapped to fixed key names, forming key-value pair record units with unique logical indexes. The tag matching degree boosting value scalar is converted from a floating-point physical quantity to a fixed-length string with dimensional description, and bound to the corresponding key-value pair field to maintain cross-platform parsing. A timestamp generation algorithm is invoked to obtain the precise time information of this reordering operation, and this timestamp is written to the time field of the operation log object to ensure that the log record has time traceability. An operation type field is generated based on the replacement path information in the reordering protocol execution result. A predefined type enumeration mapping mechanism is used to encode different types of replacement behaviors into corresponding integer identifier values ​​for subsequent machine parsing. Multidimensional feature encoding is performed on the fields such as timestamp, operation type, set of replaced clip identifiers, newly inserted position coordinate matrix, gap cluster envelope parameter, and label matching degree improvement value to form a set of multidimensional feature vectors in a unified format. This set is then stored as the value domain of the log object. Through structured encapsulation and coding standards, the results of the previous step are transformed into a standard reordering operation log with machine readability, auditability, and cross-system transmission capability, achieving full-process traceability and data consistency goals for the dynamic optimization process.

[0183] S7.4: Based on the structured reordering operation log object, use binary stream appending technology to encapsulate it into a custom metadata block that conforms to the industrial vector graphics standard, so as to generate a binary stream of metadata to be embedded, carrying full-process process feedback information.

[0184] S7.5: Based on the binary stream of the metadata to be embedded, parse the file header structure offset of the target layout image file, and perform a memory write operation to implant the binary stream of the metadata to be embedded into the reserved extension area of ​​the final layout image file header, so as to complete the generation of the final layout image file containing dynamic reordering history information.

[0185] Based on the structured input of the binary stream of the metadata to be embedded, the file header structure offset parameters of the target layout diagram file are parsed, and the file header parsing engine is used to extract the starting address and length flag of the reserved extension area of ​​the industrial vector graphics file header.

[0186] The offset parameter is verified by using an address range comparison algorithm to verify the boundary relationship between the starting address of the reserved extension area and the total length of the file, so as to ensure that subsequent memory writes will not cause out-of-bounds access errors.

[0187] Based on the verified offset parameters, the memory mapping interface is called to map the file header extension area of ​​the target layout file into a writable buffer, and a random access index table in bytes is established in the buffer.

[0188] The starting position for writing the binary stream of metadata to be embedded is located by using the buffer byte index table. The binary stream data is loaded into the mapping buffer byte by byte using a sequential write mechanism, and a position counter is maintained during the writing process to ensure data continuity.

[0189] The loaded buffer data is processed by CRC checksum calculation, and data integrity verification is achieved using the cyclic redundancy check formula. in, Indicates the first Secondary check value, For the current buffer data block, Generate a predefined polynomial.

[0190] The validated buffer contents are submitted to the file system write-back module, and a memory write operation is performed to refresh the modified file header extension area to the storage medium of the target layout file, so as to complete the generation of the final layout file containing dynamic reordering history information.

[0191] Through the above-mentioned parsing, verification, mapping, writing and verification methods, the binary stream of metadata to be embedded generated in the previous step is transformed into file header extension data carrying process audit information, thereby achieving the effect of enhancing process information at the structural level in the layout diagram file.

[0192] Step S8: The partially reordered pattern sequence is output as the final cutting instruction to the automatic cutting equipment, completing the intelligent control closed loop from fabric input to finished product output. Specifically, this includes: S8.1: Perform topological integrity verification on the pattern coordinate matrix and gap cluster envelope parameters contained in the locally reordered pattern sequence. Use the vector graphics parsing engine to extract the closed boundary point set and cutting path connectivity features of all pattern contours to generate a standard pattern path data stream with a conflict-free geometric topology.

[0193] S8.2: Based on the standard cut piece path data stream, perform process metadata fusion operation, and use binary stream embedding algorithm to encapsulate the reordering operation log generated in the previous steps, which contains the replacement cut piece identifier and the label matching degree improvement value, into a custom file header extension block to generate an enhanced layout diagram file object carrying full-process process feedback information.

[0194] S8.3: The cut piece contour vector data in the enhanced layout drawing file object is interpolated and smoothed. An adaptive spline curve fitting algorithm is used to calculate the velocity look-ahead parameter and acceleration constraint vector of the cutting tool at the corner, so as to generate a continuous trajectory motion planning sequence that meets the requirements of high dynamic response.

[0195] The coordinate point set parsing operation is performed on the cut piece outline vector data in the enhanced layout file object. The vector curve reconstruction module is called to convert the discrete point series of each cut piece boundary into the basic data structure for curve representation. Path segmentation and identification operations are performed on the parsed curve representation. A segmentation threshold is set based on the rate of curvature change, and corner transition segments and straight line segments are classified separately for differentiated processing during subsequent interpolation. An adaptive spline curve fitting algorithm is called on each segmented curve to calculate the node density coefficient based on the radius of curvature, and the interpolation accuracy of the fitted curve is adjusted using a dynamic control point generation mechanism. Speed ​​look-ahead parameters are calculated on the fitting results. Based on the length of the tool's motion trajectory curve at the corner and the preset upper limit of tool acceleration, the look-ahead speed of the tool in that path segment is calculated using the following formula: in The look-ahead speed of the tool in this section of the path. This is the proportionality coefficient. Let be the radius of curvature of the fitted curve. The tool acceleration constraint value is used. The velocity look-ahead parameter obtained from the formula is combined with the tool acceleration constraint vector to generate continuous trajectory motion planning node data for each turning path segment. The node data of each path segment are then concatenated in topological order to establish a global continuous trajectory motion planning sequence, realizing the transformation of the cutting path from a geometric representation to an executable trajectory. Through the above interpolation smoothing and dynamic parameter calculation processing methods, the enhanced nesting diagram file object from the previous step is transformed into a continuous trajectory motion planning sequence containing three-dimensional parameters of position, velocity, and acceleration, achieving trajectory execution stability and geometric accuracy of the cutting tool under high dynamic response conditions.

[0196] S8.4: Based on the continuous trajectory motion planning sequence, perform numerical control code mapping transformation, and use the post-processor to convert geometric coordinate data and velocity look-ahead parameters into a low-level pulse control instruction set that conforms to the communication protocol of the automatic cutting equipment, so as to generate the final cutting instruction data packet that can directly drive the servo motor to perform precise cutting action.

[0197] S8.5: Perform real-time transmission and handshake confirmation operations on the final cutting instruction data packet, send the instruction stream to the motion control unit of the automatic cutting equipment through the industrial fieldbus protocol, and monitor the ready status feedback signal at the equipment end to complete the intelligent control closed loop of the entire process from fabric input to finished product output.

[0198] For those skilled in the art, various other corresponding changes and modifications can be made based on the technical solutions and concepts described above, and all such changes and modifications should fall within the protection scope of the claims of this invention.

[0199] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this application pertains. The terms “first,” “second,” “third,” and similar terms used in this patent application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an” or “a” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “comprising” or “including” and similar terms mean that the element or object preceding “comprising” or “including” encompasses the element or object listed following “comprising” or “including” and its equivalents, and do not exclude other elements or objects. The “multiple” mentioned in the embodiments of this application refers to two or more. A and / or B indicate three possibilities: A; B; and A and B.

[0200] The above description is merely an exemplary embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An automatic pattern making and cutting method for yoga clothing production fabric, specifically including: S1: Deploy a sensor array at the yoga garment sewing station, which includes a fabric micro-strain thin film sensor, a high-frequency acoustic pickup and an infrared hot spot imaging module, to synchronously collect multi-dimensional physical signals during the sewing process to obtain sewing condition data stream. S2: Input the sewing condition data stream into a pre-trained temporal neural network model for feature mapping processing to generate a set of process labels including sewing extension tolerance, edge anchoring stability and stitch density compatibility. S3: Construct a main queue based on the pre-sorted cut pieces in descending order of area, and establish an auxiliary queue associated with the process label set to form a dual queue architecture for storing cut pieces to be arranged and real-time feedback labels. S4: Monitor the set of process tags in the auxiliary queue and determine whether the latest received suture extension tolerance value is lower than a preset stable threshold to trigger local reordering; S5: If it is determined that a local reordering is triggered, multiple minimum area cut pieces that have not yet been arranged are extracted from the tail of the main queue to form a set of candidate cut pieces, and alternative cut piece sets are selected according to the current edge anchoring stability level. S6: Search for a gap cluster that can accommodate the replacement piece set within the non-critical stress zone of the already arranged layout, swap the positions of the replacement piece set and the original pieces in the gap cluster, and adjust the geometric arrangement to generate a pattern sequence. S7: Based on the replaced piece identifier, insertion position coordinates, and gap cluster envelope parameters of the layout sequence, construct a reordering operation log containing the label matching degree improvement value and embed it into the header of the final layout image file.

2. The automatic pattern making and cutting method for yoga clothing production fabric according to claim 1, characterized in that, Step S7 is followed by step S8, which specifically includes: S8: The rearranged pattern sequence after partial reordering is output as the final cutting instruction to the automatic cutting equipment to complete the intelligent control closed loop from fabric input to finished product output.

3. The automatic pattern making and cutting method for yoga clothing production fabric according to claim 1, characterized in that, Step S2 specifically includes: The input sewing condition data stream is processed by time window sliding segmentation. The frame synchronization alignment algorithm is used to perform time domain registration on the fabric micro-strain feature data, acoustic pattern amplitude feature data and infrared hot spot temperature feature data to generate a multimodal temporal feature tensor with a unified spatiotemporal reference as the model input object. Based on the multimodal temporal feature tensor, a pre-trained temporal neural network model is invoked for forward inference calculation. The gated recurrent unit is used to extract long-short-term dependencies and perform weighted fusion using an attention mechanism to output a high-dimensional hidden layer state vector sequence that represents the dynamic evolution of the sewing process. The high-dimensional hidden layer state vector sequence is subjected to fully connected layer mapping transformation processing. The hidden layer feature distribution is projected onto the three-dimensional process semantic space using the soft maximum activation function to generate initial probability distribution vectors corresponding to elastic deformation recovery capability, edge warping resistance capability, and stitch density adaptation capability, respectively. Based on the initial probability distribution vector, a normalized scalar transformation operation is performed, and a linear interpolation algorithm is used to map the probability values ​​into process quantification indicators with clear physical dimensions, so as to generate scalars containing specific values ​​for suture extension tolerance, edge anchoring stability, and stitch density compatibility. The generated scalars of stitch extension tolerance, edge anchoring stability, and stitch density compatibility are subjected to structured encapsulation. The quantification indicators of the three dimensions are bound to the current timestamp using a key-value pair assembly protocol to generate a set of process tags that can be directly called by the nesting engine.

4. The automatic pattern making and cutting method for yoga clothing production fabric according to claim 1, characterized in that, Step S4 specifically includes: The process tag set stored in the auxiliary queue is polled and read in a round-robin fashion to obtain the latest target process tag set generated within the current time window as the initial input data; Based on the dimensional field information contained in the target process label set, a key-value parsing operation is performed to extract the numerical feature quantity of sewing stretch tolerance, which characterizes the elastic deformation recovery ability of the fabric under sewing tension. The process stability threshold parameter fixed inside the embedded nesting controller is used to perform boundary comparison operation on the numerical characteristic of the suture extension tolerance to generate a deviation judgment flag bit; Execute conditional branch jump processing based on the logical state of the deviation determination flag to generate the local reordering used to indicate whether to start or maintain the current nesting process; The execution context of the main control flow is updated based on the state of the local reordering to achieve seamless switching control from static nesting mode to dynamic local reordering mode.

5. The method according to claim 1, characterized in that, The fabric micro-strain thin-film sensor is deployed above the feed dog at the sewing station based on the piezoresistive effect. The measuring axis is aligned with the direction of the fabric fibers. It is attached using a constant pressure rolling process, and the sampling period is set to a short sampling interval that matches the sewing speed and the requirements for micro-strain dynamic response.

6. The method according to claim 1, characterized in that, The generation of the process label set includes: feature synchronization, normalization, temporal segmentation, gated recurrent units, attention mechanism and fully connected mapping, using a soft maximum activation function, and the output is a normalized probability value.

7. The method according to claim 1, characterized in that, The physical dimension mapping ranges of the process label set are as follows: the seam stretch tolerance corresponds to the process allowable stretch range under the elastic modulus of the fabric, the edge anchoring stability corresponds to the torque stability range under the bending stiffness of the cut piece, and the stitch density compatibility corresponds to the standard process range of sewing stitch density.

8. The method according to claim 1, characterized in that, The process of selecting alternative cut pieces specifically includes: selecting the smallest unarranged cut pieces extracted from the tail of the main queue according to a preset edge anchoring stability threshold, and outputting the preferred cut pieces that meet the high edge anchoring stability requirements through multi-dimensional feature matching and Boolean masking mechanism.

9. The method according to claim 1, characterized in that, When selecting alternative cut pieces, the edge anchoring stability level is determined based on the edge anchoring stability evaluation parameters of the cut pieces. The edge anchoring stability evaluation parameters include: Young's modulus based on the stiffness level of the cut piece material, moment of inertia based on the cross-sectional dimensions of the cut piece, and critical warping stress based on the wrinkle resistance properties of the fabric.

10. The method according to claim 1, characterized in that, When the search finds a gap cluster that can accommodate the alternative cut pieces set, polygon Boolean operations and convex hull generation algorithms are used. The area of ​​the outer rectangle of the gap cluster is adapted to the size range of the cut pieces to be arranged, and the spatial features are aligned according to rotation normalization. The fit score adopts a normalized scoring mechanism.

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